/*-
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 *
 */

package org.nd4j.linalg.api.ops.impl.shape;

import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.ops.DynamicCustomOp;

import java.util.Arrays;
import java.util.List;

/**
 * Broadcast function
 *
 * @author Adam Gibson
 */
public class Broadcast extends DynamicCustomOp {
    private long[] shape;
    public Broadcast(SameDiff sameDiff,SDVariable iX, long[] shape) {
        super(null,sameDiff,new SDVariable[]{iX});
        this.shape = shape;
    }


    public Broadcast() {}


    @Override
    public List<long[]> calculateOutputShape() {
        return Arrays.asList(shape);
    }

    @Override
    public String opName() {
        return "broadcast";
    }



    @Override
    public List<SDVariable> doDiff(List<SDVariable> i_v) {
        throw new UnsupportedOperationException();
    }

    @Override
    public String onnxName() {
        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
    }

    @Override
    public String tensorflowName() {
        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
    }

}
